Elamur Research⌕
☰
home / authors / Kalpa Gunaratna

Kalpa Gunaratna

Kalpa Gunaratna has 2 papers in our library, in 2023–2026, most of them on Information retrieval & search; often with Hai Wang, Jun Yan, Lichang Chen.

2 papers in our library · 2023–2026

OpenAlex · ORCID · h-index 12 · 52 works · 400 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 208 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #5523

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🏢SamsungSeoul · South Korea2021–2026 · 2 paperscurrent🏢Samsung ElectronicsSeoul · South Korea2026 · 2 paperscurrent🎓Wright State UniversityDayton · United States2010–2019 · 14 papers🏢SamsungRidgefield Park · United States2020–2022 · 4 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksComputer Science · 2 worksArts and Humanities · 1 workLanguage and Linguistics · 1 workComputer Vision and Pattern Recognition · 1 work

🧭 Topics 1Research subjects and languages the work is about.Information retrieval & search · 1 work

 Works by year

2020s

20261 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. AlpaGasus: Training A Better Alpaca with Fewer Data (2023 · 220 citations)
    Instruction-tuning datasets produced by a teacher model often contain flawed examples. The 52k Alpaca set, generated by Text-Davinci-003, includes many responses that are wrong or off-topic. Such examples can mislead the fine-tuned model and make training cost more. Earlier cleanup efforts like Alpaca-cleaned…
  2. IMRNNs: An Efficient Method for Interpretable Dense Retrieval via Embedding Modulation (2026)
    Dense retrievers in RAG pipelines encode queries and documents into fixed vectors on their own. As a result, a document's representation cannot change depending on the query, and users cannot see why a document was ranked highly. The authors present IMRNNs, a lightweight add-on that works over a frozen encoder.…

All works

4 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

  1. AlpaGasus: Training A Better Alpaca with Fewer DataarXiv:2307.08701Lichang Chen, Shiyang Li, Jun Yan, et al.2023 · 220 citations
  2. FACES: Diversity-Aware Entity Summarization Using Incremental Hierarchical Conceptual Clustering ↗Proceedings of the AAAI Conference on Artificial Intelligence · no summary yetKalpa Gunaratna, Krishnaprasad Thirunarayan, Amit Sheth2015 · 67 citations
  3. IMRNNs: An Efficient Method for Interpretable Dense Retrieval via Embedding ModulationFindings of the Association for Computational LinguisticsYash Saxena, Ankur Padia, Kalpa Gunaratna, et al.2026
  4. Switching Heads and Softening Tokens: Turnkey Solutions to Visually Grounded Document QA ↗Findings of the Association for Computational Linguistics: ACL 2026 · no summary yetXiming Wen et al.2026

 Co-authors here

7 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 208 times (of the 149,439 papers whose bibliographies we read); 42 citing papers with a page here →.

Updated

This page in Markdown → https://md.research.elamur.ai/free/en/authors/kalpa-gunaratna/